In DQN, what is the real reason we don't backpropagate through the target network?
$begingroup$
Actually, if we are to backpropagate through the target network, there is no use for the target network anymore.
Let's say we don't use any target network. We hence enforce only the "temporal difference" allowing gradients to flow through both ways i.e. q(s,) and q(s',).
This completes the gradient and prevents known instabilities of DQN. While this training is much more stable, it is not widely used.
My question is why not? Where is the catch?
deep-learning reinforcement-learning
New contributor
$endgroup$
add a comment |
$begingroup$
Actually, if we are to backpropagate through the target network, there is no use for the target network anymore.
Let's say we don't use any target network. We hence enforce only the "temporal difference" allowing gradients to flow through both ways i.e. q(s,) and q(s',).
This completes the gradient and prevents known instabilities of DQN. While this training is much more stable, it is not widely used.
My question is why not? Where is the catch?
deep-learning reinforcement-learning
New contributor
$endgroup$
add a comment |
$begingroup$
Actually, if we are to backpropagate through the target network, there is no use for the target network anymore.
Let's say we don't use any target network. We hence enforce only the "temporal difference" allowing gradients to flow through both ways i.e. q(s,) and q(s',).
This completes the gradient and prevents known instabilities of DQN. While this training is much more stable, it is not widely used.
My question is why not? Where is the catch?
deep-learning reinforcement-learning
New contributor
$endgroup$
Actually, if we are to backpropagate through the target network, there is no use for the target network anymore.
Let's say we don't use any target network. We hence enforce only the "temporal difference" allowing gradients to flow through both ways i.e. q(s,) and q(s',).
This completes the gradient and prevents known instabilities of DQN. While this training is much more stable, it is not widely used.
My question is why not? Where is the catch?
deep-learning reinforcement-learning
deep-learning reinforcement-learning
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New contributor
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PhizazPhizaz
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Phizaz is a new contributor. Be nice, and check out our Code of Conduct.
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